endogenous-macrodynamics-in-algorithmic-recourse
Software and data underlying the publication: Endogenous Macrodynamics in Algorithmic Recourse
Description
Code and research results for SaTML 2023 research paper. Originally released here: https://github.com/pat-alt/endogenous-macrodynamics-in-algorithmic-recourse.
The research results include:
Folders with images that went into a) the body of the paper or b) the online companion.Folders with results (.jls; .csv) for different experiments: a) synthetic data; b) real-world data; and, c) mitigation strategies for both categories of datasets (see paper for details on experiments). Results for all categories are further grouped by dataset.For each dataset, results include: a) "experiment.jls" files that can be loaded into a Julia session: the loaded Julia objects are structs that contain all settings characterizing a specific experiment. b) "output.csv" files that contain the final experimental outputs: estimated counterfactual evaluation metrics groups by model and counterfactual explainer.
- MIT
Reference papers
Mentions
- 1.Author(s): Giovanni De Toni, Stefano Teso, Bruno Lepri, Andrea PasseriniPublished in Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency by ACM in 2025, page: 89-10710.1145/3715275.3732008
- 2.Author(s): Andrew Bell, Joao Fonseca, Carlo Abrate, Francesco Bonchi, Julia StoyanovichPublished in Proceedings of the 5th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization by ACM in 2025, page: 170-18410.1145/3757887.3763014
- 3.Author(s): João Fonseca, Andrew Bell, Carlo Abrate, Francesco Bonchi, Julia StoyanovichPublished in Equity and Access in Algorithms, Mechanisms, and Optimization by ACM in 2023, page: 1-1110.1145/3617694.3623251